2. visual perception: optimizing information visualization regarding the human visual ... ·...

LMU München – Medieninformatik – Andreas Butz – Informationsvisualisierung – WS2011/12 Folie

2. Visual Perception:Optimizing Information Visualization regarding the human visual system

Vorlesung „Informationsvisualisierung”Prof. Dr. Andreas Butz, WS 2011/12Konzept und Basis für Folien: Thorsten Büring

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Outline• Perception Definition & Context• Preattentive processing• Gestalt Laws• Change Blindness• Data encoding – glyphs• Data encoding – color• Characteristics of Visual Properties

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Perceptual Processing• Design visual information to be efficiently

perceivable – quick, unambiguous• Need to understand how human visual perception

and information processing works• Perception science related to:

–Physiology: study the physical, biochemical and information processing functions of living organisms

–Cognitive psychology: studying internal mental processes• how do people learn, understand, solve problems with regard

to sensory information?

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Model of Perceptual Processing• Numerous other models exist• Simplified 3-stage model: many subsystems

involved in human vision• Stage 1 – rapid parallel processing to extract low-

level properties of a visual scene–Detection of shape, spatial attributes, orientation, color,

texture, movement–Billions of Neurons work in parallel, extracting information

simultaneously –Occurs automatically, independent of (cognitive) focus– Information is transitory (though briefly held in a short-

lived visual buffer)–Often called “preattentive” processing

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Model of Perceptual Processing

Image taken from Ware 20015


Model of Perceptual Processing• Stage 2 – pull out structures via pattern perception

– Visual field is divided in simple patterns: e.g. continuous contours, regions of the same color / texture

– Object recognition– Slower serial processing

• Stage 3 – sequential goal-directed processing– Information is further reduced to a few objects held in visual

working memory– Used to answer and construct visual queries– Attention-driven - forms the basis for visual thinking– Interfaces to other subsystems:

• Verbal linguistic: connection of words and images• Perception-for-action: motor system to control muscle movement

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Example

• Route between the two letters?• Stage 1: automatic parallel extraction

of colors, shapes, position etc.• Stage 2:

–Pattern finding of black contours (lines) between two symbols (letters)

• Stage 3: –Few objects are held in working memory at a time– Identify path sequentially (formulate new visual query)

• In this lecture we will focus on aspects related to stage 1 & 2 of the model

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Preattentive Processing• A limited set of basic visual properties are

processed preattentively• Information that “pops out”• Parallel processing by the low-level visual system

(Stage 1 in the model) • Occurs prior to conscious attention• Important for designing effective visualizations

–What features can be perceived rapidly?–Which properties are good discriminators?–What can mislead viewers?–How to design information such that it pops out?

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Example: Find the 3s

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Example: Find the 3s

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Preattentive Processing• How to find out if a visual attribute is preattentive?• Measure response time for tasks

– Detection of a target among distractors – Is the target present?

– Boundary detection – Do items form two groups?– Counting – How many targets are there?

• Detection of targets on a large multi-element display – < 200 to 250 ms are considered preattentive – Eye movement takes at least 200 ms to initiate

• Example: is there a red target present in the images?

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Color• Is there a red circle present in the image?

Images taken from http://www.csc.ncsu.edu/faculty/healey/PP/index.html

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Color• Is there a red circle present in the image?

Color is preattentively processed!

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Shape• Is there a red circle present in the image?

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Shape• Is there a red circle present in the image?

Shape is preattentively processed!

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Color & Shape• Is there a red circle present in the image?

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Color & Shape• Is there a red circle present in the image?

Conjunction of 2 properties is usually not preattentive!

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Boundary Detection• Do items form a boundary? If yes, based on which

attribute(s)?

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Boundary Detection• Do items form a boundary? If yes, based on which

attribute(s)?

Conjunction search: grouping by hue and shape

Preattentive: grouping by hue

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Common Preattentive Properties• Color

–Hue–Intensity–Motion–Flicker–Direction of motion–Spatial Position–2D position–Stereoscopic depth–Convexity / Concavity


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• Form–Line orientation–Line length–Line width–Size–Curvature–Shape–Spatial grouping


Conjunction Search

• A target with a unique visual property (e.g., shape OR color) “pops out”

• Conjunction target is made up of non-unique features–Requires a time-consuming serial

search, e.g.– For every red colored item: is it a circle? – For every cricular item: is it red?

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Cognition and Gestalt Laws• See „Digitale Medien“, Chapter 3a (typography)• Recap: step 2 of the visual information processing model –

pattern and object recognition using the raw data collected in step 1

• Based on which visual properties do we structure the data?• Gestalt school of psychology founded in 1912 formulated

Gestalt laws• “The whole is greater than the sum of parts” (Koffka 1935)• Laws still useful today, but not the neural mechanisms

proposed• Perception: An introduction to the Gestalt-theorie (Kurt

Koffka, 1922): http://psychclassics.yorku.ca/Koffka/Perception/perception.htm

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http://psychclassics.yorku.ca/Koffka/Perception/perception.htm





Example of Gestalt perception

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What do you see?• Can you find the dog?• Dalmatinian exploring a leave covered forest floor• Once you have found it, try to think of the picture

as a simple pattern of black and white again• Does it work?• Mind tries to detect anything meaningful by

identifying patterns• Different tools are tried sequentially• Perceptual organization is a powerful mechanism

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GL: Grouping by Spatial Proximity• Columns or rows?• Small difference in spacing causes change in perception• Use proximity to emphasize between display items• To which group (top / bottom) does the x dot belong?

Spacing is equal for both groups!• Spatial concentration principle: we group regions of similar

element density (Slocum1983)

x

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GL: Similarity • Rows or columns?• Similar elements tend to be grouped together

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GL: Connectedness• Palmer & Rock 1994• Potentially more powerful organizing principle than

proximity, color, size, shape

proximity color size shape Example: node-link diagram

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GL: Continuity• Example circuit design – understanding how

components are connected

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GL: Symmetry

• Example of how symmetry detection may be exploited for visual data mining

• Support the search for similar patterns in time-series plots (measurements of deep ocean drilling cores)

Image taken from Ware 2001

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GL: Area• Smaller components of a pattern tend to be

perceived as an object • White propeller and black propeller

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GL: Figure & Ground• Figure: something object-like that is perceived

being in the foreground• Ground: whatever lies behind the figure• Fundamental perceptual act of identifying objects• All Gestalt laws contribute, e.g., closed contour,

symmetry, area• Equally balanced cues for figure and ground can

result in bistable perception

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GL: Common Fate• Objects moving in the same direction are

perceived as an entity• Example taken from: http://tepserver.ucsd.edu/

~jlevin/gp/time-example-common-fate/

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Outline• Perception Definition & Context• Preattentive processing• Gestalt Laws• Change + Inattentional Blindness• Data encoding – glyphs• Data encoding – color• Characteristics of Visual Properties

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Change Blindness (CB)• Example: old style aircraft altimeter

– Thinnest hand indicates number of tens of thousands of feet

– Next larger hand number of thousands of feet– Quick glance after interruption results in

misinterpretation if the change in the display is not noticed

– Difference of ten thousand feet

• Phenomenon: inability to detect changes in visual scenes– mid-eye movement– mid-blink– Flicker (short blanking of screen)– Gradual change

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Change Blindness (CB)• Participants of a study were found unable to detect a

change from one person to another in midconversation (Simson & Levin 1998)

• Sample principle: insensitivity to changes of objects in movie scenes interrupted by a cut (Levin & Simons 1997)

• Various examples: http://viscog.beckman.uiuc.edu/djs_lab/demos.html

• Problem related to the short-lived visual buffer and the very limited capacity of our visual working memory

• Need to emphasize changes• In some applications changes may be distracting, e.g.

ambient information visualization -> utilize CB37

http://viscog.beckman.uiuc.edu/djs_lab/demos.html





CB: Flicker Example 1

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

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CB: Flicker Example 2

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

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CB: Gradual Change Example

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Recognition is primed by expectation• http://www.youtube.com/watch?

v=kjtSfTCrMm4&feature=related

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http://www.youtube.com/watch?v=kjtSfTCrMm4&feature=related





Inattentional Blindness• Basketball passes: http://www.youtube.com/

watch?v=Ahg6qcgoay4&feature=fvw• Color changing card trick: http://www.youtube.com/

watch?v=hDLLf_WCyZ4&hl=de

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http://www.youtube.com/watch?v=Ahg6qcgoay4&feature=fvw




http://www.youtube.com/watch?v=hDLLf_WCyZ4&hl=de





Perception Summary

• It is not enough to simply show something.• We need to pay attention when and how it

is shown.• Otherwise people might miss it or take

forever to find it.

• Apply your knowledge about perception to check whether your visualizations are effective!

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Encoding Data with Glyphs• Glyph: graphical object designed to convey multiple data values• Multidimensional discrete data, e.g. a collection of cars with

several attributes each, e.g. horsepower, weight, acceleration etc.• What visual properties can be mapped to the attributes?• FilmFinder example

– Color to film genre– X-position to year of release– Y-position to popularity

• Additional properties– Lightness– Shape– Orientation– Texture– Motion– Blinking

FilmFinder (www.cs.umd.edu/) 47


Encoding Data with Glyphs• Limitations of low-level graphical attributes for glyph design• Easily resolvable steps of a visual property

– 12 colors (for preattentive processing only 8 colors)– About 4 orientation steps– At most 4 size steps– Binary blink coding (on / off)– Texture unknown– Shape unkown

• Mixing visual properties– Properties are not independent from each other, e.g. blink coding

interferes with motion coding– Conjunctions are usually non-preattentive– Some dimensions are integral– Best to restrict the mapping to color, shape, spatial position (and motion)

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Color Vision & Model• Human color vision

– Sensory response to electromagnetic radiation in the spectrum 0.4 – 0.7 micrometers

– Based on three dimensions (three different types of color receptors in human retina)

• Powerful encoding potential: compared to gray scales the number of just noticeable differences is much higher

• About 8% of the male and 1% of the female population are color-blind

• Color Model HSV (aka HSB)– Hue - blue, green, etc. (X axis)– Saturation – intensity of color (Y axis)– Value – light/dark (slider)

HSV cone representation

HSV 2D color picker

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Color Scales• Definition: pictorial representation of a set of

distinct categorical or numerical values, where each value is assigned its own color (Levkovitz 1996)

• Desired properties of perception–Preserve the order of the data values, if any–Uniform distance between adjacent values (i.e. equally

spaced numerical steps are perceived as equally spaced perceptual steps)

–No artificial boundaries that do not exist in the data (i.e. continuously present continuous values)

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Color Rules I• Always ensure a reasonable luminance contrast

between foreground and background color – chromatic variation may not be enough!

• Black and white borders around colored symbols can reduce contrast effects

• Canonical colors (close to an ideal) are easier to remember

• Only a small set of basic colors should be used for nominal (distinct) labeling– At most 12 colors: red, green, yellow, blue, black, white,

pink, cyan, gray, orange, brown, purple– The first four colors are “hard-wired” into the human brain –

should be used with priority

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Grayscale• Usually not considered a color scale, but very common• Provides simple and natural sense of order • Disadvantages

– Limited number of just-noticeable-differences (JNDs) about 60 to 90– Contrast effects can significantly reduce accuracy– Luminance channel is fundamental to much of perception –

grayscale encoding may be considered “a waste of perceptual resources” (Ware, 2000)

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Rainbow for Ordering Data?• Most common: rainbow scale for ordinal and

quantitative (spectral colors)–Continuous spectrum –Common arbitrary division in 8 or less named colors (red,

orange, yellow, green, cyan, blue, indigo, violet)• Problems with rainbow scale

–Can you order the color blocks from low to high?–Yellow (in the middle of the scale) may draw too much

attention, when users are seeking for extreme values–Perception of non-existing boundaries

John Stasko54


Recommended Color Scales• Ordinal data

–Low saturation to high saturation (single hue) - also very limited JNDs

–Dark to light (single hue)–Red to green, yellow to blue, red to blue

• Ratio (hardly feasible) / diverging data–Neutral value (e.g. white) to represent zero– Increases in saturation toward distinct colors for positive

and negative values (double-ended multiple hue)• Look up www.colorbrewer2.org for inspiration

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http://www.colorbrewer2.org

http://www.colorbrewer2.org


colorbrewer2.org

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Redundant Color Scales• Use multiple color properties to redundantly represent data• Visual reinforcement of steps• Overcome visual deficiencies• Redundant model components: data values are mapped to both hue and

brightness• Heated-object scale

– Going from black to white passing through orange and yellow– Monotonic increase in brightness provides more natural ordering than rainbow scale

• Linearized optimal color scale– Scale maximizing the number of JNDs while preserving a (more or less) natural order

Silva et al. 200757


Color Scale• US presidential elections - Bush & RNC's

campaign funding

$7,000,000

$20

http://fundrace.huffingtonpost.com/moneymap.php?cand=Bush&zoom=County

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Color Scale• Vote distribution of 2004 US presidential election -

the darker the color, the more of a landslide it was for the winning party

Republican

Democrat

Neutral

http://fundrace.huffingtonpost.com/moneymap.php

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Color Scale

Sheelagh Carpendale

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Color Scale

Sheelagh Carpendale

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Color Rules II• For larger areas on a white background use low-saturation

light colors• Small color-coded objects should be given high saturation• Use red and green in the center of the field of view (edges

of retina not sensitive for these)• Use black, white, yellow in periphery• Use color for grouping and search• Color Blindness Simulator: http://www.etre.com/tools/

colourblindsimulator/ • Generation of color families

– Use canonical colors– Family members should differ by saturation – Better: saturation and lightness

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http://www.etre.com/tools/colourblindsimulator/





Bivariate Color Coding• Recap: color is three-dimensional• Two data dimensions may be mapped to different color

dimensions (e.g. hue and saturation, hue and lightness)• Problem: bivariate color coding has been found

notoriously difficult to read (Wainer & Francolini, 1980)• The same applies to multidimensional color coding

– E.g. amount of red, amount of green, amount of blue for coding colored dots in scatterplot (Ware & Beatty 1988)

– Clusters could be easily identified by the participants of a user test– Precise decoding of the color components difficult

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Example: Bivariate Color Coding

Image by Cynthia Brewer, http://www.psu.edu/ur/2003/MapColorphotos.html

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Characteristics of Visual Properties

• Some properties possess intrinsic meaning– Density with Grayscale: the darker the

more– Size / Length / Area: the larger the more– Position: depending on culture, in Europe

the leftmost / topmost are first– Color: depending on culture, e.g. white

associated with death in Japan

• Accuracy of representations for quantitative measures (empirically verified by Cleveland & McGill, 1985)

(Mackinlay, 1988)

Attribute Quantitative Qualitative

Line length x

2D position x

Orientation x

Line width x

Size x

Shape x

Curvature x

Enclosure x

Hue x

Intensity x

(Few, 2004)66

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